rlmFit: Fitter Functions for Robust Linear Models

In LVSmiRNA: LVS normalization for Agilent miRNA data

Description

These are the basic computing engines called by RLM used to fit robust linear models. These should not be used directly unless by experienced users.

Usage

rlmFit(x, y, maxit=20L, k=1.345, offset=NULL,method=c("joint","rlm"),
cov.formula=c("weighted","asymptotic"),start=NULL, error.limit=0.01)

Arguments

x	design matrix of dimension n * p.
y	vector of observations of length n, or a matrix with n rows.
maxit	the limit on the number of IWLS iterations.
k	tuning constant used for Huber proposal 2 scale estimation.
offset	numeric of length n. This can be used to specify an a priori known component to be included in the linear predictor during fitting.
method	currently, only method="rlm.fit" is supported.
cov.formula	are the methods to compute covariance matrix, currently either weighted or asymptotic.
start	vector containing starting values for the paramter estimates.
error.limit	the convergence criteria during iterative estimation.

Value

a list with components

coeffecients	p vector
Std.Error	p vector
t.value	p vector
cov.matrix	matrix of dimension p*p
res.SD	value of residual standard deviation

...

Author(s)

Stefano Calza <[e-mail]>, Suo Chen and Yudi Pawitan.

References

Yudi Pawitan: In All Likelihood: Statistical modeling and inference using likelihood. Oxford University Press. 2001.

See Also

RLM which you should use for robust linear regression usually.

Examples

set.seed(133)
n <- 9 
p <- 3
X <- matrix(rnorm(n * p), n,p) #no intercept
y <- rnorm(n)

RLM.fit <- rlmFit (x=X, y=y)

LVSmiRNA documentation built on Oct. 31, 2019, 5:29 a.m.